c++实现mlp神经网络
2024-08-24 19:23:50
之前一直用theano训练样本，最近需要转成c或c++实现。在网上参考了一下其它代码，还是喜欢c++。但是看了几份cpp代码之后，发现都多少有些bug，很不爽。由于本人编码能力较弱，还花了不少时间改正。另外又添加了写权值和读权值的功能，可以保存训练的结果。下面是代码实现的基本功能描述。
问题描述：
用cpp重写mlp，即普通的多层神经网络。需要实现多个隐藏层与输出层互连，分类层采用softmax分类。
测试例子：
测试例子为自己构造，将3位二进制转为10进制有8中可能，我分别让它们对应label0-7。例如：001对应的label为1,111对应的label为7，以此类推
下面看代码：
main.cpp
#include <iostream>
#include "NeuralNetwork.h"
#include "util.h"
using namespace std;
/*main函数中调用的两个函数功能一样
*将3位二进制分类成十进制
*test_lr用的是单层的softmax回归
*mlp是含有多个隐藏层的神经网络
*/
int main()
{
cout << "****softmax****" << endl;
test_lr();
cout << "****mlp****" << endl;
mlp();
return 0;
}
test_lr函数为softmax测试，先看它的相关文件
LogisticRegression.h
#ifndef LOGISTICREGRESSIONLAYER
#define LOGISTICREGRESSIONLAYER class LogisticRegression
{
public:
LogisticRegression(int n_i, int i_o, int);
~LogisticRegression(); void forward_propagation(double* input_data);
void back_propagation(double* input_data, double* label, double lr);
void softmax(double* x);
void printwb();
void writewb(const char *pcname);
long readwb(const char *pcname, long);
void setwb(double ppdw[][3], double [8]);
void train(double *x, double *y, double lr); int predict(double *);
double cal_error(double **ppdtest, double* pdlabel, int ibatch);
//double cal_error(double* label);
void makeLabels(int* pimax, double (*pplabels)[8]); //本层前向传播的输出值，也是最终的预测值
double* output_data;
//反向传播时所需值
double* delta; public:
int n_in;
int n_out;
int n_train;
double** w;
double* b; }; void test_lr();
void testwb();
#endif
头文件中的testwb为测试权值的读写而测试的，可以先不用理会
LogisticRegression.cpp
#include <cmath>
#include <ctime>
#include <iostream>
#include "LogisticRegression.h"
#include "util.h" using namespace std; LogisticRegression::LogisticRegression(int n_i, int n_o, int n_t)
{
n_in = n_i;
n_out = n_o;
n_train = n_t; w = new double* [n_out];
for(int i = 0; i < n_out; ++i)
{
w[i] = new double [n_in];
}
b = new double [n_out]; double a = 1.0 / n_in; srand((unsigned)time(NULL));
for(int i = 0; i < n_out; ++i)
{
for(int j = 0; j < n_in; ++j)
w[i][j] = uniform(-a, a);
b[i] = uniform(-a, a);
} delta = new double [n_out];
output_data = new double [n_out];
} LogisticRegression::~LogisticRegression()
{
for(int i=0; i < n_out; i++)
delete []w[i];
delete[] w;
delete[] b;
delete[] output_data;
delete[] delta;
} void LogisticRegression::printwb()
{
cout << "'****w****\n";
for(int i = 0; i < n_out; ++i)
{
for(int j = 0; j < n_in; ++j)
cout << w[i][j] << ' ';
cout << endl;
//w[i][j] = uniform(-a, a);
}
cout << "'****b****\n";
for(int i = 0; i < n_out; ++i)
{
cout << b[i] << ' ';
}
cout << endl;
cout << "'****output****\n";
for(int i = 0; i < n_out; ++i)
{
cout << output_data[i] << ' ';
}
cout << endl; }
void LogisticRegression::softmax(double* x)
{
double _max = 0.0;
double _sum = 0.0; for(int i = 0; i < n_out; ++i)
{
if(_max < x[i])
_max = x[i];
}
for(int i = 0; i < n_out; ++i)
{
x[i] = exp(x[i]-_max);
_sum += x[i];
} for(int i = 0; i < n_out; ++i)
{
x[i] /= _sum;
}
} void LogisticRegression::forward_propagation(double* input_data)
{
for(int i = 0; i < n_out; ++i)
{
output_data[i] = 0.0;
for(int j = 0; j < n_in; ++j)
{
output_data[i] += w[i][j]*input_data[j];
}
output_data[i] += b[i];
} softmax(output_data);
} void LogisticRegression::back_propagation(double* input_data, double* label, double lr)
{
for(int i = 0; i < n_out; ++i)
{
delta[i] = label[i] - output_data[i] ;
for(int j = 0; j < n_in; ++j)
{
w[i][j] += lr * delta[i] * input_data[j] / n_train;
}
b[i] += lr * delta[i] / n_train;
}
} int LogisticRegression::predict(double *x)
{
forward_propagation(x);
cout << "***result is ***" << endl;
int iresult = getMaxIndex(output_data, n_out);
cout << iresult << endl;
if (iresult == 1)
printArr(output_data, n_out);
return iresult; }
void LogisticRegression::train(double *x, double *y, double lr)
{
forward_propagation(x);
back_propagation(x, y, lr);
}
//这个函数目前还用不到，大家不用看
double LogisticRegression::cal_error(double **ppdtest, double* pdlabel, int ibatch)
{
double error = 0.0, dmax = 0;
int imax = -1, ierrNum = 0;
for (int i = 0; i < ibatch; ++i)
{
imax = predict(ppdtest[i]);
if (imax != pdlabel[i])
++ierrNum;
}
error = (double)ierrNum / ibatch;
return error;
}
void LogisticRegression::writewb(const char *pcname)
{
savewb(pcname, w, b, n_out, n_in);
}
long LogisticRegression::readwb(const char *pcname, long dstartpos)
{
return loadwb(pcname, w, b, n_out, n_in, dstartpos);
} void LogisticRegression::setwb(double ppdw[][3], double szib[8])
{
for (int i = 0; i < n_out; ++i)
{
for (int j = 0; j < n_in; ++j)
w[i][j] = ppdw[i][j];
b[i] = szib[i];
}
cout << "setwb----------" << endl;
printArrDouble(w, n_out, n_in);
printArr(b, n_out);
} void LogisticRegression::makeLabels(int* pimax, double (*pplabels)[8])
{
for (int i = 0; i < n_train; ++i)
{
for (int j = 0; j < n_out; ++j)
pplabels[i][j] = 0;
int k = pimax[i];
pplabels[i][k] = 1.0;
}
} void test_lr()
{
srand(0); double learning_rate = 0.1;
double n_epochs = 200; int test_N = 2;
const int trainNum = 8, n_in = 3, n_out = 8;
//int n_out = 2;
double train_X[trainNum][n_in] = {
{1, 1, 1},
{1, 1, 0},
{1, 0, 1},
{1, 0, 0},
{0, 1, 1},
{0, 1, 0},
{0, 0, 1},
{0, 0, 0}
};
//szimax存储的是最大值的下标
int szimax[trainNum];
for (int i = 0; i < trainNum; ++i)
szimax[i] = trainNum - i - 1;
double train_Y[trainNum][n_out]; // construct LogisticRegression
LogisticRegression classifier(n_in, n_out, trainNum); classifier.makeLabels(szimax, train_Y);
// train online
for(int epoch=0; epoch<n_epochs; epoch++) {
for(int i=0; i<trainNum; i++) {
//classifier.trainEfficient(train_X[i], train_Y[i], learning_rate);
classifier.train(train_X[i], train_Y[i], learning_rate);
}
} const char *pcfile = "test.wb";
classifier.writewb(pcfile); LogisticRegression logistic(n_in, n_out, trainNum);
logistic.readwb(pcfile, 0);
// test data
double test_X[2][n_out] = {
{1, 0, 1},
{0, 0, 1}
};
// test
cout << "before readwb ---------" << endl;
for(int i=0; i<test_N; i++) {
classifier.predict(test_X[i]);
cout << endl;
}
cout << "after readwb ---------" << endl;
for(int i=0; i<trainNum; i++) {
logistic.predict(train_X[i]);
cout << endl;
}
cout << "*********\n"; }
void testwb()
{ int test_N = 2;
const int trainNum = 8, n_in = 3, n_out = 8;
//int n_out = 2;
double train_X[trainNum][n_in] = {
{1, 1, 1},
{1, 1, 0},
{1, 0, 1},
{1, 0, 0},
{0, 1, 1},
{0, 1, 0},
{0, 0, 1},
{0, 0, 0}
};
double szib[n_out] = {1, 2, 3, 3, 3, 3, 2, 1}; // construct LogisticRegression
LogisticRegression classifier(n_in, n_out, trainNum); classifier.setwb(train_X, szib); const char *pcfile = "test.wb";
classifier.writewb(pcfile); LogisticRegression logistic(n_in, n_out, trainNum);
logistic.readwb(pcfile, 0); }
下面是mlp神经网络组合起来的代码,这个就是将隐藏层与softmax层互连，当时调试了好久好久。。。
NeuralNetwork.h
#ifndef NEURALNETWORK_H
#define NEURALNETWORK_H #include "HiddenLayer.h"
#include "LogisticRegression.h" class NeuralNetwork
{
public:
NeuralNetwork(int n, int n_i, int n_o, int nhl, int*hls);
~NeuralNetwork(); void train(double** in_data, double** in_label, double lr, int epochs);
void predict(double** in_data, int n); void writewb(const char *pcname);
void readwb(const char *pcname); private:
int N; //样本数量
int n_in; //输入维数
int n_out; //输出维数
int n_hidden_layer; //隐层数目
int* hidden_layer_size; //中间隐层的大小 e.g. {3,4}表示有两个隐层，第一个有三个节点，第二个有4个节点 HiddenLayer **sigmoid_layers;
LogisticRegression *log_layer;
}; void mlp(); #endif
mlp()就是测试整个神经网络基本功能的代码，写的比较乱。基本就是先用一个mlp训练，然后保存权值。之后另一个mlp读取权值，预测结果。
NeuralNetwork.cpp
#include <iostream>
#include "NeuralNetwork.h"
#include "util.h"
//#include "HiddenLayer.h"
//#include "LogisticRegression.h" using namespace std; const int n_train = 8, innode = 3, outnode = 8;
NeuralNetwork::NeuralNetwork(int n, int n_i, int n_o, int nhl, int *hls)
{
N = n;
n_in = n_i;
n_out = n_o; n_hidden_layer = nhl;
hidden_layer_size = hls; //构造网络结构
sigmoid_layers = new HiddenLayer* [n_hidden_layer];
for(int i = 0; i < n_hidden_layer; ++i)
{
if(i == 0)
{
sigmoid_layers[i] = new HiddenLayer(n_in, hidden_layer_size[i]);//第一个隐层
}
else
{
sigmoid_layers[i] = new HiddenLayer(hidden_layer_size[i-1], hidden_layer_size[i]);//其他隐层
}
} log_layer = new LogisticRegression(hidden_layer_size[n_hidden_layer-1], n_out, N);//最后的softmax层
} NeuralNetwork::~NeuralNetwork()
{
//二维指针分配的对象不一定是二维数组
for(int i = 0; i < n_hidden_layer; ++i)
delete sigmoid_layers[i]; //删除的时候不能加[]
delete[] sigmoid_layers;
//log_layer只是一个普通的对象指针，不能作为数组delete
delete log_layer;//删除的时候不能加[]
} void NeuralNetwork::train(double** ppdinData, double** ppdinLabel, double dlr, int iepochs)
{
printArrDouble(ppdinData, N, n_in); cout << "******label****" << endl;
printArrDouble(ppdinLabel, N, n_out); //反复迭代样本iepochs次训练
for(int epoch = 0; epoch < iepochs; ++epoch)
{
double e = 0.0;
for(int i = 0; i < N; ++i)
{
//前向传播阶段
for(int n = 0; n < n_hidden_layer; ++ n)
{
if(n == 0) //第一个隐层直接输入数据
{
sigmoid_layers[n]->forward_propagation(ppdinData[i]);
}
else //其他隐层用前一层的输出作为输入数据
{
sigmoid_layers[n]->forward_propagation(sigmoid_layers[n-1]->output_data);
}
}
//softmax层使用最后一个隐层的输出作为输入数据
log_layer->forward_propagation(sigmoid_layers[n_hidden_layer-1]->output_data); //e += log_layer->cal_error(ppdinLabel[i]); //反向传播阶段
log_layer->back_propagation(sigmoid_layers[n_hidden_layer-1]->output_data, ppdinLabel[i], dlr); for(int n = n_hidden_layer-1; n >= 1; --n)
{
if(n == n_hidden_layer-1)
{
sigmoid_layers[n]->back_propagation(sigmoid_layers[n-1]->output_data,
log_layer->delta, log_layer->w, log_layer->n_out, dlr, N);
}
else
{
double *pdinputData;
pdinputData = sigmoid_layers[n-1]->output_data; sigmoid_layers[n]->back_propagation(pdinputData,
sigmoid_layers[n+1]->delta, sigmoid_layers[n+1]->w, sigmoid_layers[n+1]->n_out, dlr, N);
}
}
//这里该怎么写？
if (n_hidden_layer > 1)
sigmoid_layers[0]->back_propagation(ppdinData[i],
sigmoid_layers[1]->delta, sigmoid_layers[1]->w, sigmoid_layers[1]->n_out, dlr, N);
else
sigmoid_layers[0]->back_propagation(ppdinData[i],
log_layer->delta, log_layer->w, log_layer->n_out, dlr, N);
}
//if (epoch % 100 == 1)
//cout << "iepochs number is " << epoch << " cost function is " << e / (double)N << endl;
} } void NeuralNetwork::predict(double** ppdata, int n)
{ for(int i = 0; i < n; ++i)
{
for(int n = 0; n < n_hidden_layer; ++ n)
{
if(n == 0) //第一个隐层直接输入数据
{
sigmoid_layers[n]->forward_propagation(ppdata[i]);
}
else //其他隐层用前一层的输出作为输入数据
{
sigmoid_layers[n]->forward_propagation(sigmoid_layers[n-1]->output_data);
}
}
//softmax层使用最后一个隐层的输出作为输入数据
log_layer->predict(sigmoid_layers[n_hidden_layer-1]->output_data);
//log_layer->forward_propagation(sigmoid_layers[n_hidden_layer-1]->output_data);
}
} void NeuralNetwork::writewb(const char *pcname)
{
for(int i = 0; i < n_hidden_layer; ++i)
{
sigmoid_layers[i]->writewb(pcname);
}
log_layer->writewb(pcname); }
void NeuralNetwork::readwb(const char *pcname)
{
long dcurpos = 0, dreadsize = 0;
for(int i = 0; i < n_hidden_layer; ++i)
{
dreadsize = sigmoid_layers[i]->readwb(pcname, dcurpos);
cout << "hiddenlayer " << i + 1 << " read bytes: " << dreadsize << endl;
if (-1 != dreadsize)
dcurpos += dreadsize;
else
{
cout << "read wb error from HiddenLayer" << endl;
return;
}
}
dreadsize = log_layer->readwb(pcname, dcurpos);
if (-1 != dreadsize)
dcurpos += dreadsize;
else
{
cout << "read wb error from sofmaxLayer" << endl;
return;
}
}
//double **makeLabelSample(double **label_x)
double **makeLabelSample(double label_x[][outnode])
{
double **pplabelSample;
pplabelSample = new double*[n_train];
for (int i = 0; i < n_train; ++i)
{
pplabelSample[i] = new double[outnode];
} for (int i = 0; i < n_train; ++i)
{
for (int j = 0; j < outnode; ++j)
pplabelSample[i][j] = label_x[i][j];
}
return pplabelSample;
}
double **maken_train(double train_x[][innode])
{
double **ppn_train;
ppn_train = new double*[n_train];
for (int i = 0; i < n_train; ++i)
{
ppn_train[i] = new double[innode];
} for (int i = 0; i < n_train; ++i)
{
for (int j = 0; j < innode; ++j)
ppn_train[i][j] = train_x[i][j];
}
return ppn_train;
}
void mlp()
{
//输入样本
double X[n_train][innode]= {
{0,0,0},{0,0,1},{0,1,0},{0,1,1},{1,0,0},{1,0,1},{1,1,0},{1,1,1}
}; double Y[n_train][outnode]={
{1, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0, 0},
{0, 0, 0, 1, 0, 0, 0, 0},
{0, 0, 0, 0, 1, 0, 0, 0},
{0, 0, 0, 0, 0, 1, 0, 0},
{0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 0, 1},
};
const int ihiddenSize = 2;
int phidden[ihiddenSize] = {5, 5};
//printArr(phidden, 1);
NeuralNetwork neural(n_train, innode, outnode, ihiddenSize, phidden);
double **train_x, **ppdlabel;
train_x = maken_train(X);
//printArrDouble(train_x, n_train, innode);
ppdlabel = makeLabelSample(Y);
neural.train(train_x, ppdlabel, 0.1, 3500);
cout<<"trainning complete..."<<endl;
//pcname存放权值
const char *pcname = "mlp55new.wb";
neural.writewb(pcname);
NeuralNetwork neural2(n_train, innode, outnode, ihiddenSize, phidden);
cout<<"readwb start..."<<endl;
neural2.readwb(pcname);
cout<<"readwb end..."<<endl;
neural.predict(train_x, n_train);
cout << "----------after readwb________" << endl;
neural2.predict(train_x, n_train); for (int i = 0; i != n_train; ++i)
{
delete []train_x[i];
delete []ppdlabel[i];
}
delete []train_x;
delete []ppdlabel;
cout<<endl;
}
HiddenLayer.h
#ifndef HIDDENLAYER_H
#define HIDDENLAYER_H class HiddenLayer{
public:
HiddenLayer(int n_i, int n_o);
~HiddenLayer(); void forward_propagation(double* input_data); void back_propagation(double *pdinputData, double *pdnextLayerDelta,
double** ppdnextLayerW, int iNextLayerOutNum, double dlr, int N); void writewb(const char *pcname);
long readwb(const char *pcname, long); //本层前向传播的输出值,作为下一层的输入值
double* output_data;
//反向传播时所需值
double* delta; public:
int n_in;
int n_out;
double** w;
double*b;
}; #endif
HiddenLayer.cpp
#include <cmath>
#include <cstdlib>
#include <ctime>
#include <iostream>
#include "HiddenLayer.h"
#include "util.h" using namespace std; HiddenLayer::HiddenLayer(int n_i, int n_o)
{
n_in = n_i;
n_out = n_o; w = new double* [n_out];
for(int i = 0; i < n_out; ++i)
{
w[i] = new double [n_in];
}
b = new double [n_out]; double a = 1.0 / n_in; srand((unsigned)time(NULL));
for(int i = 0; i < n_out; ++i)
{
for(int j = 0; j < n_in; ++j)
w[i][j] = uniform(-a, a);
b[i] = uniform(-a, a);
} delta = new double [n_out];
output_data = new double [n_out];
} HiddenLayer::~HiddenLayer()
{
for(int i=0; i<n_out; i++)
delete []w[i];
delete[] w;
delete[] b;
delete[] output_data;
delete[] delta;
}
void HiddenLayer::forward_propagation(double* pdinputData)
{
for(int i = 0; i < n_out; ++i)
{
output_data[i] = 0.0;
for(int j = 0; j < n_in; ++j)
{
output_data[i] += w[i][j]*pdinputData[j];
}
output_data[i] += b[i]; output_data[i] = sigmoid(output_data[i]);
}
} void HiddenLayer::back_propagation(double *pdinputData, double *pdnextLayerDelta,
double** ppdnextLayerW, int iNextLayerOutNum, double dlr, int N)
{
/*
pdinputData 为输入数据
*pdnextLayerDelta 为下一层的残差值delta,是一个大小为iNextLayerOutNum的数组
**ppdnextLayerW 为此层到下一层的权值
iNextLayerOutNum 实际上就是下一层的n_out
dlr 为学习率learning rate
N 为训练样本总数
*/ //sigma元素个数应与本层单元个数一致，而网上代码有误
//作者是没有自己测试啊，测试啊
//double* sigma = new double[iNextLayerOutNum];
double* sigma = new double[n_out];
//double sigma[10];
for(int i = 0; i < n_out; ++i)
sigma[i] = 0.0; for(int i = 0; i < iNextLayerOutNum; ++i)
{
for(int j = 0; j < n_out; ++j)
{
sigma[j] += ppdnextLayerW[i][j] * pdnextLayerDelta[i];
}
}
//计算得到本层的残差delta
for(int i = 0; i < n_out; ++i)
{
delta[i] = sigma[i] * output_data[i] * (1 - output_data[i]);
} //调整本层的权值w
for(int i = 0; i < n_out; ++i)
{
for(int j = 0; j < n_in; ++j)
{
w[i][j] += dlr * delta[i] * pdinputData[j];
}
b[i] += dlr * delta[i];
}
delete[] sigma;
} void HiddenLayer::writewb(const char *pcname)
{
savewb(pcname, w, b, n_out, n_in);
}
long HiddenLayer::readwb(const char *pcname, long dstartpos)
{
return loadwb(pcname, w, b, n_out, n_in, dstartpos);
}
下面是一个工具文件
util.h
#ifndef UTIL_H
#define UTIL_H typedef unsigned char BYTE;
double sigmoid(double x); double uniform(double _min, double _max);
//void printArr(T *parr, int num);
//void printArrDouble(double **pparr, int row, int col);
void initArr(double *parr, int num);
int getMaxIndex(double *pdarr, int num);
void savewb(const char *pcname, double **ppw, double *pb,
int irow, int icol);
long loadwb(const char *pcname, double **ppw, double *pb,
int irow, int icol, long dstartpos);
void readonefile(const char *pcname);
void writeonefile(const char *pcname); template <typename T>
void printArr(T *parr, int num)
{
cout << "****printArr****" << endl; for (int i = 0; i < num; ++i)
cout << parr[i] << ' ';
cout << endl;
}
template <typename T>
void printArrDouble(T **pparr, int row, int col)
{
cout << "****printArrDouble****" << endl;
for (int i = 0; i < row; ++i)
{
for (int j = 0; j < col; ++j)
{
cout << pparr[i][j] << ' ';
}
cout << endl;
}
} #endif
util.cpp
#include "util.h"
#include <iostream>
#include <ctime>
#include <cmath> using namespace std; int getMaxIndex(double *pdarr, int num)
{
double dmax = -1;
int imax = -1;
for(int i = 0; i < num; ++i)
{
if (pdarr[i] > dmax)
{
dmax = pdarr[i];
imax = i;
}
}
return imax;
} double sigmoid(double x)
{
return 1.0/(1.0+exp(-x));
} double uniform(double _min, double _max)
{
return rand()/(RAND_MAX + 1.0) * (_max - _min) + _min;
} void initArr(double *parr, int num)
{
for (int i = 0; i < num; ++i)
parr[i] = 0.0;
} void savewb(const char *pcname, double **ppw, double *pb,
int irow, int icol)
{
FILE *pf;
if( (pf = fopen(pcname, "ab" )) == NULL )
{
printf( "File coulkd not be opened " );
return;
} int isizeofelem = sizeof(double);
for (int i = 0; i < irow; ++i)
{
if (fwrite((const void*)ppw[i], isizeofelem, icol, pf) != icol)
{
fputs ("Writing ppw error",stderr);
return;
}
}
if (fwrite((const void*)pb, isizeofelem, irow, pf) != irow)
{
fputs ("Writing ppw error",stderr);
return;
}
fclose(pf);
}
long loadwb(const char *pcname, double **ppw, double *pb,
int irow, int icol, long dstartpos)
{
FILE *pf;
long dtotalbyte = 0, dreadsize;
if( (pf = fopen(pcname, "rb" )) == NULL )
{
printf( "File coulkd not be opened " );
return -1;
}
//让文件指针偏移到正确位置
fseek(pf, dstartpos , SEEK_SET); int isizeofelem = sizeof(double);
for (int i = 0; i < irow; ++i)
{
dreadsize = fread((void*)ppw[i], isizeofelem, icol, pf);
if (dreadsize != icol)
{
fputs ("Reading ppw error",stderr);
return -1;
}
//每次成功读取，都要加到dtotalbyte中，最后返回
dtotalbyte += dreadsize;
}
dreadsize = fread(pb, isizeofelem, irow, pf);
if (dreadsize != irow)
{
fputs ("Reading pb error",stderr);
return -1;
}
dtotalbyte += dreadsize;
dtotalbyte *= isizeofelem;
fclose(pf);
return dtotalbyte;
}
void readonefile(const char *pcname)
{
FILE *pf;
if( (pf = fopen(pcname, "rb" )) == NULL )
{
printf( "File could not be opened " );
return;
} /*int isizeofelem = sizeof(BYTE);
BYTE ielem;*/
int isizeofelem = sizeof(double);
double ielem;
while(1 == fread((void*)(&ielem), isizeofelem, 1, pf))
cout << ielem << endl; fclose(pf);
}
void writeonefile(const char *pcname)
{
FILE *pf;
if( (pf = fopen(pcname, "wb" )) == NULL )
{
printf( "File could not be opened " );
return;
} //int isizeofelem = sizeof(BYTE);
//BYTE ielem = (BYTE)16;
int isizeofelem = sizeof(int);
int ielem = 16;
if(1 == fwrite((void*)(&ielem), isizeofelem, 1, pf))
cout << ielem << endl; fclose(pf);
}
至此代码已经贴完了，我测试是可以运行的。本人编码较少，如果有什么问题，请见谅。
这里有vs2008建的工程代码，如果不想自己建工程，可以下载运行即可，工程里面只是少了保存权值的函数而已。
下载地址： 点击打开链接
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